Parallel Materialization of Large ABoxes

This paper is concerned with the efficient computation of materialization in a knowledge base with a large ABox. We present a framework for performing this task on a shared-nothing parallel machine. The framework partitions TBox and ABox axioms using a min-min strategy. It utilizes an existing system, like SwiftOWLIM, to perform local inference computations and coordinates exchange of relevant information between processors. Our approach is able to exploit parallelism in the axioms of the TBox to achieve speedup in a cluster. However, this approach is limited by the complexity of the TBox. We present an experimental evaluation of the framework using datasets from the Lehigh University Benchmark (LUBM).